{"id":"W2570556144","doi":"10.1016/j.ijrefrig.2017.01.005","title":"Sensitivity analysis and multiobjective optimization of a parallel-plate active magnetic regenerator using a genetic algorithm","year":2017,"lang":"en","type":"article","venue":"International Journal of Refrigeration","topic":"Advanced Thermodynamics and Statistical Mechanics","field":"Physics and Astronomy","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Hydro-Québec","keywords":"Regenerative heat exchanger; Sensitivity (control systems); Crossover; Refrigerant; Genetic algorithm; Multi-objective optimization; Pareto principle; Heat exchanger; Coefficient of performance; Exergy efficiency; Magnetic refrigeration; Mathematical optimization; Mass flow rate; Computer science; Materials science; Control theory (sociology); Exergy; Engineering; Mathematics; Mechanical engineering; Process engineering; Mechanics; Physics; Magnetic field; Control (management); Electronic engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001272282,0.00008653532,0.0001985035,0.0001402216,0.0001149473,0.0000884494,0.00008962159,0.00002876703,0.00003495855],"category_scores_gemma":[0.00004346483,0.00007943255,0.00008426737,0.00004825389,0.00004382696,0.0002833442,0.00003347172,0.00008805589,2.045391e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004654267,"about_ca_system_score_gemma":0.00004508285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001324007,"about_ca_topic_score_gemma":0.00001121526,"domain_scores_codex":[0.9992456,0.00005665941,0.0002984017,0.0001106513,0.0002185615,0.00007012696],"domain_scores_gemma":[0.9983441,0.00005345845,0.0007490055,0.00009924355,0.0007092013,0.00004501459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001982319,0.0001344426,0.0026249,0.000003410968,0.001303864,0.00003161248,0.0003901347,0.7344847,0.01358073,0.009523636,8.636155e-7,0.2377235],"study_design_scores_gemma":[0.0005331134,0.00006818517,0.0112666,0.00001961264,0.0002039165,0.00001071724,0.00006704251,0.9788718,0.002755921,0.006122766,0.000002784198,0.00007760198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.242093,0.00001484838,0.7575392,0.00002781141,0.0001414742,0.00004904389,0.00008931737,0.000001151167,0.00004417692],"genre_scores_gemma":[0.7445129,0.0000139918,0.2553112,0.000004373648,0.0001291513,7.187095e-7,0.00001195968,0.000005333662,0.00001039607],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5024199,"threshold_uncertainty_score":0.3239165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009021770008969547,"score_gpt":0.2864853220140609,"score_spread":0.2774635520050913,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}